Bridging Data and Decision-Making: Data Visualization Techniques with R

Nnamdi Azikwe University

Ifeoma Egbogah

Data

What is Data?

Data refers to raw facts, figures, and statistics that are collected through observation, measurement, research, or experimentation. On their own, data have no meaning until they are organized, analyzed, and interpreted.

Key Characteristics of Data:

  • Raw: Unprocessed and unorganized

  • Factual: Based on real-world events, measurements, or records

Data Types

Numerical or Quantitative Data

Numerical (or Quantitative) Data refers to data that represents measurable quantities—that is, values that can be counted or measured and expressed in numbers.

Data Types Contd.

Numerical or Quantitative Data

Continuous Data Discrete Data
Data that can take any value within a range. Data that can take only specific, separate values.
Usually measured (can include decimals/fractions). Usually countable (no decimals)

Examples:

  • Height of a person (e.g., 1.75 meters)

  • Temperature (e.g., 36.6°C)

  • Sales revenue (e.g., ₦1,254,500.75)

Examples:

  • Number of employees in a company (e.g., 15, 23, 50)

  • Number of students in a classroom

  • Number of cars sold in a day

Data Types Contd.

Key Features of Numerical Data:

  • Can be compared, ordered, added, or averaged

  • Suitable for mathematical and statistical analysis

  • Often visualized using bar charts, histograms, line graphs, or scatter plots

Data Types Contd.

Categorical or Qualitative Data

Categorical (or Qualitative) Data refers to data that describes qualities or characteristics. Instead of numbers, it uses labels, names, or categories to represent information.

Data Types Contd.

Key Feature of Categorical Data:

  • Descriptive rather than numerical

  • Used to classify or group data

  • Cannot be meaningfully added, subtracted, or averaged

  • Can be visualized using bar charts, pie charts, or tables

Why Data Visualization Matters

  • Humans process visuals 60,000x faster than text

  • Visuals simplify complex data

  • Helps identify trends, outliers, and patterns

  • Supports data-driven decisions

Real-World Example

COVID-19

COVID-19 dashboards helped governments track and respond to infection spikes and vaccinations.

Bridging the Gap Between Data and Decisions

Mind the Gap

Problem: Data is abundant, but insights are scarce.

Solution: Visualization bridges the gap between raw data and strategic action.

Outcome: Simplifies storytelling and supports real-time decisions.

What is R and Why Use It?

R

  • Free and open-source statistical language

  • Used in academia and business

  • Integrates data wrangling, analysis, and visualization

Key Visualization Packages:

ggplot2

plotly

shiny

Data Visualization

Understanding Visualization Types

Chart Type Best For
Line Chart Trends over time
Bar Chart Comparing categories
Scatter Plot Correlations, relationships
Maps Geospatial data
Dashboard Monitoring KPIs in real-time

Tip: Choose simplicity and clarity over complexity.

Case Study

Academic Use Case – Education Access

Dataset: World Bank (Literacy vs Internet Access)

Visualization: Scatter plot showing socio-economic development.

Insight: Nigeria lags behind Kenya and Egypt in internet penetration despite comparable literacy rates.